📰 News
- [2024.06.12] Release model weights and the first version of the technical report of VideoLLaMA 2.
- [2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.
🌎 Model Zoo
🚀 Main Results
Multi-Choice Video QA & Video Captioning
Open-Ended Video QA
🤖 Inference with VideoLLaMA2
import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
def inference():
disable_torch_init()
# Video Inference
modal = 'video'
modal_path = 'assets/cat_and_chicken.mp4'
instruct = 'What animals are in the video, what are they doing, and how does the video feel?'
# Image Inference
modal = 'image'
modal_path = 'assets/sora.png'
instruct = 'What is the woman wearing, what is she doing, and how does the image feel?'
model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-16F'
model, processor, tokenizer = model_init(model_path)
output = mm_infer(processor[modal](modal_path), instruct, model=model, tokenizer=tokenizer, do_sample=False, modal=modal)
print(output)
if __name__ == "__main__":
inference()
Citation
If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:
@article{damonlpsg2024videollama2,
title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
journal={arXiv preprint arXiv:2406.07476},
year={2024},
url = {https://arxiv.org/abs/2406.07476}
}
@article{damonlpsg2023videollama,
title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
author = {Zhang, Hang and Li, Xin and Bing, Lidong},
journal = {arXiv preprint arXiv:2306.02858},
year = {2023},
url = {https://arxiv.org/abs/2306.02858}
}